Security and Sustainability of Tourist Destinations Through Digital Technologies: A Comparative Analysis of Almaty and Belgrade

Abstract

Contemporary digital technologies have become key instruments in enhancing the security and sustainability of tourist destinations. This study explores the role of digital solutions such as smart surveillance systems, mobile applications, artificial intelligence, and the Internet of Things (IoT) in strengthening tourist safety and supporting long-term sustainable development. The theoretical framework is based on the Norm Activation Model (NAM), employing the constructs of Awareness of Consequences, Ascription of Responsibility, Personal Norms, and Behavioral Intention, expanded by the construct of Sustainability Outcomes. This research was conducted as a comparative case study of Almaty (Kazakhstan) and Belgrade (Serbia), using a structured questionnaire and quantitative analysis. The findings indicate that tourists’ perceptions of security, mediated by digital technologies, significantly shape their behavioural intentions and contribute to sustainable destination outcomes. The study provides theoretical implications for the advancement of the NAM in tourism, as well as practical guidelines for destination managers in developing a safe and sustainable environment.

1. Introduction

Tourism, as one of the most dynamic industries in recent decades, faces the dual challenge of ensuring visitor safety and maintaining the long-term sustainability of destinations, with the perception of safety recognised as a key factor in tourists’ decision-making [1,2,3]. At the same time, global processes such as climate change, urban growth, and increasing pressure on resources require the adoption of sustainable tourism management models. In this context, digital technologies represent a crucial driver of the transformation of tourist destinations [4]. Their application enables more efficient management of spatial flows, timely communication with tourists, enhanced institutional transparency, and the development of innovative services that simultaneously improve both security and sustainability [2,5]. Smart cities and smart destinations integrate technologies such as artificial intelligence, the Internet of Things (IoT), surveillance systems, and mobile applications to build interconnected and resilient tourism environments [5,6]. The role of digital technologies is particularly important in reducing risks and building trust between tourists and local communities [4,7]. The implementation of smart surveillance systems and security applications contributes to strengthening the subjective sense of safety, while improved digital infrastructure ensures greater control and more effective responses to potential crisis situations [8]. At the same time, the digitalisation of resource management and the introduction of environmentally responsible technological solutions support long-term sustainability and reduce the negative impacts of tourism on the environment [8,9].

Although an increasing number of studies analyse the application of digital technologies in tourism, their effects on tourists’ behavioural intentions in the context of security and sustainability remain insufficiently explored [10]. Furthermore, comparative research encompassing different urban tourist centres is rare, even though the comparison of diverse socio-economic and cultural contexts can reveal both universal and context-specific patterns in tourist behaviour [10,11]. Considering this, the present study applies the theoretical framework of the Norm Activation Model (NAM), which provides deeper insights into how awareness of consequences, ascription of responsibility, and personal norms influence tourists’ intentions [12,13]. The model is further extended with the construct of “Sustainability Outcomes” in order to incorporate indicators linking security with the long-term sustainability of tourist destinations.

While the Norm Activation Model (NAM) has been extensively applied in environmental studies and, to some extent, in tourism research, it has not previously been extended with the construct of Sustainability Outcomes in the domain of digital security in tourism. This study therefore introduces a novel perspective by linking digital technologies for safety with long-term sustainability outcomes, thus enriching the NAM framework and addressing a significant gap in the literature. By doing so, it allows for a deeper theoretical understanding of how tourists’ awareness of consequences, responsibility, and personal norms translate into behavioural intentions within digitally enhanced, sustainable destinations.

The aim of the research is to examine how digital technologies affect perceptions of security and sustainability in tourist destinations, with particular reference to Almaty (Kazakhstan) and Belgrade (Serbia), and to determine their role in shaping tourists’ behavioural intentions. Conducting the study in two different tourist destinations is of particular importance, as it enables comparative analysis across diverse cultural, institutional, and socio-economic contexts. Almaty represents a dynamically growing destination in Central Asia with pronounced processes of digitalisation and modernisation, while Belgrade is one of the key tourist hubs in Southeast Europe with its specific historical, cultural, and infrastructural framework. Comparing these two cities allows for the identification of similarities and differences in the perception and acceptance of digital technologies, thereby contributing to a deeper understanding of the role of cultural and institutional factors in strengthening the security and sustainability of tourism. In this way, the study not only tests the applicability of the Norm Activation Model (NAM) in different settings but also offers guidance to policymakers and destination managers on adapting digital transformation strategies to specific local conditions.

2. Literature Review

2.1. Digital Technologies in Tourism

The development of digital technologies over the past few decades has radically transformed the functioning of the tourism industry, the structure of the tourism market, and the habits of tourists themselves [4]. Whereas the tourism sector once relied on traditional services and direct communication, it is now largely digitalised and integrated into global technological flows. As one of the most sensitive industries, tourism is particularly vulnerable to global changes and crises; however, thanks to digitalisation, it has become more resilient, flexible, and capable of responding to diverse challenges [14,15]. Digital platforms, mobile applications, smart surveillance systems, artificial intelligence, and the Internet of Things (IoT) are no longer mere additions to the tourism offer but central instruments in the creation and implementation of strategies that link security, efficiency, and sustainability [16,17]. Their role is multifaceted: from enhancing the tourist experience and resource management to building the image of destinations as modern, reliable, and innovative.

The concept of “smart tourist destinations” represents the pinnacle of technology–tourism integration. It involves the establishment of systems in which everything is interconnected: infrastructure, transport, hospitality, cultural institutions, and individual tourists as active users [18,19]. Mobile applications have become essential tools through which tourists can obtain real-time information about attractions, safety procedures, weather conditions, or environmentally friendly accommodation options [19,20]. Applications also enable direct interaction with institutions, thereby building trust and increasing transparency. Smart surveillance systems play a crucial role in creating a sense of security [21]. They combine cameras, sensors, and analytical software that not only record events but also predict risks based on observed patterns. In this way, security ceases to be a passive reaction to problems and becomes an active and preventive process [21,22].

Artificial intelligence (AI) in tourism is employed for predictive analytics, that is, for forecasting tourist behaviour, creating personalised services, and optimising movement flows within a destination [23]. For example, machine learning algorithms can suggest the best time to visit a particular attraction in order to avoid crowds or recommend environmentally friendly transport options. The Internet of Things (IoT) integrates devices, vehicles, buildings, and infrastructure into a unified network that generates real-time data [23,24]. In tourism, IoT is used to monitor energy consumption in hotels, measure air quality in urban areas, detect crowding, or track tourist movements. These data are then analysed and utilised to improve both the security and sustainability of the destination [25].

Most contemporary tourists expect all necessary information to be available immediately. This applies not only to basic destination data but also to safety protocols, crisis guidelines, and opportunities for ensuring that their travel aligns with principles of sustainable development [26]. Such accessibility increases trust and strengthens the image of the destination as responsible and modern [27]. Institutions and destination managers employ digital tools to collect and analyse data in real time. These data enable more efficient risk management, better control of tourist flows, more rational use of resources, and the design of evidence-based marketing strategies. In this way, a two-way communication channel is established between the destination and tourists: tourists receive security and service quality, while destinations obtain feedback and the ability to adapt to market needs [28,29].

2.2. Security in Tourist Destinations

Security represents one of the key prerequisites for tourism development and a factor that directly influences tourists’ decisions when choosing a destination [1,30]. Even the most attractive destinations lose their appeal if there is a perception of high security risk [31]. In the contemporary era, when tourists are exposed to vast amounts of information through the media and social networks, the perception of safety can often be just as important as the actual situation [32]. For this reason, destinations that aim to remain competitive in the global market must invest in digital technologies that ensure visitor safety and foster trust [33,34].

Traditional forms of control, such as the physical presence of police, patrols, or conventional video surveillance cameras, are no longer sufficient to address the complex security challenges in tourism [35,36]. Modern destinations increasingly introduce smart surveillance systems that combine high-resolution cameras, motion sensors, and software for facial or behavioural recognition [37]. These systems not only record events but also enable advanced analytics and predictive risk management [38]. Based on such analyses, institutions can respond in a timely manner and prevent incidents before they occur, significantly enhancing the level of safety within the tourism environment [39,40,41].

Mobile applications have become one of the most powerful and accessible tools for strengthening security in tourism [42]. Modern tourists can use applications to receive real-time notifications about potential risks, changes in safety procedures, or crisis situations [43]. Many applications also include integrated functions for quick contact with emergency services, the police, embassies, or tourist information centres, thereby significantly reducing uncertainty [43,44]. In addition, mobile applications enable two-way communication, as tourists can report problems or suspicious situations, which contributes to more effective crisis management [45,46]. In this way, technology becomes a mediator that facilitates cooperation between tourists and institutions.

Artificial intelligence (AI) is increasingly being applied in the field of security. Machine learning algorithms can analyse the movement of large groups of people, predict areas where congestion or conflicts may occur, and propose solutions for optimising flows [47,48]. For instance, in major tourist centres, AI can direct movement patterns to prevent overcrowding at specific locations [49]. Beyond physical safety, artificial intelligence plays a crucial role in cybersecurity. With the growing prevalence of electronic payments and digital bookings, tourists are increasingly exposed to risks of data misuse or financial fraud [50,51]. AI-based systems can identify atypical behavioural patterns and block transactions that indicate potential fraud, thereby enhancing trust in digital services [51,52].

The Internet of Things (IoT) represents one of the most innovative forms of support for tourism security [17]. By connecting various digital devices into a unified system, IoT enables real-time monitoring of conditions in the tourism environment [17,53]. Sensors installed in airport terminals, hotels, or tourist zones can measure visitor numbers, temperature, humidity, noise levels, or even concentrations of pollutants. The collected data are sent to centralised systems for analysis, allowing institutions to respond promptly [17,52,54]. Thus, IoT serves not only as a tool for control but also for risk prevention, as it enables the rapid identification of potential problems and their resolution before escalation [44,55].

When applied in an integrated manner, all these technologies build a significantly higher level of trust among tourists [56]. The perception that a destination is “smart” and technologically advanced creates a sense of security that can often be decisive in travel choices [12,57]. At the same time, this sense of security encourages tourists to stay longer, spend more, and recommend the destination to others after their visit. In this way, digital technologies in the field of security function not only as protection mechanisms but also as strategic instruments for building long-term loyalty and a positive destination image [58,59,60,61].

2.3. Sustainability and Technologies in Tourism

Sustainability is one of the fundamental principles of contemporary tourism and represents the foundation of the long-term competitiveness of tourist destinations. Tourism development that is not aligned with the principles of sustainability can have negative consequences for the environment, cultural heritage, and the social structure of local communities [61,62]. Uncontrolled growth in tourist numbers leads to the overexploitation of natural resources, the destruction of ecosystems, and the erosion of cultural authenticity. For this reason, contemporary tourism is increasingly oriented towards management models that combine economic development with environmental protection, the preservation of cultural heritage, and the strengthening of social stability [62,63]. Digital technologies play a central role in this process, as they enable the creation of innovative solutions for risk prevention, more efficient resource management, and more active involvement of the local community in the tourism system [63,64].

In this context, smart surveillance systems have a dual role. In addition to their primary function of enhancing security, they also serve as instruments for managing tourist flows [65]. The analysis of movement patterns makes it possible to identify parts of a destination where excessive concentrations of tourists occur [65,66]. In such situations, systems can propose redirection towards less congested zones, thereby reducing pressure on infrastructure and natural resources while simultaneously protecting sensitive areas. In this way, technology contributes to the preservation of cultural heritage, the protection of biodiversity, and the overall sustainability of tourist destinations [54,56,66].

Mobile applications are increasingly assuming the role of mediators in building tourists’ ecological awareness. Through applications, tourists can obtain information about environmentally friendly transport options, hotels that use renewable energy sources, or restaurants that apply the “farm-to-table” concept [67,68]. In this sense, applications serve not only for navigation and information but also for encouraging responsible behaviour. Tourists can also actively participate in the preservation of destinations via applications, for instance by reporting illegal waste disposal or excessive pollution [69]. Artificial intelligence (AI) enables new models of resource management through the analysis of large datasets. AI systems can monitor energy and water consumption in hotels in real time, assess tourist carrying capacities, or analyse the impact of tourism on the local economy. Based on these analyses, destinations can forecast demand in different seasons, avoid infrastructure overload, and plan investments that support long-term development [67,68,69,70]. AI also enables the prediction of environmental risks such as pollution or climate extremes, allowing preventive protection measures to be planned in advance [70,71].

The Internet of Things (IoT) plays a crucial role in ecological and institutional sustainability. Networked sensors can monitor air quality, waste levels, water consumption, or noise pollution. Based on the collected data, automated optimisation systems are activate, for example, smart irrigation systems in parks, energy consumption optimisation in hotels, or traffic management in tourist zones [71,72,73]. In this way, IoT enables more efficient resource use, a reduction in the carbon footprint, and improved management of both urban and natural spaces [73,74].

By integrating these technologies, destinations can achieve a dual effect: enhancing tourist security and ensuring long-term sustainability. Linking security systems with environmental indicators leads to the creation of “smart and resilient destinations,” which tourists perceive as modern, reliable, and responsible [64,70,73,74]. Such destinations enjoy a greater competitive advantage because they simultaneously provide safety and a high-quality, environmentally responsible experience. This interconnection represents the foundation of a new approach to tourism development in the 21st century, in which digital technologies become a key instrument in building safe, sustainable, and attractive tourism environments [73,74].

Previous applications of the Norm Activation Model (NAM) in tourism have largely focused on environmental decision-making and general tourist behaviour, such as environmentally responsible practices or sustainable consumption choices. However, there is a notable lack of studies that employ NAM to analyse tourists’ behavioural intentions in the context of digital security. Given that digital technologies are now central to shaping both safety perceptions and long-term sustainability in tourism, extending NAM to this domain addresses an important gap and provides a more comprehensive understanding of tourist behaviour in contemporary digitally enhanced destinations [75,76,77,78,79,80,81,82].

According to the analysed literature and previous research, hypotheses were formulated in line with the NAM, extended with the construct of Sustainability Outcomes, and the methodology defined:

H1a. 

Awareness of consequences has a positive effect on the ascription of responsibility among tourists in the tourist destination of Almaty.

H1b. 

Awareness of consequences has a positive effect on the ascription of responsibility among tourists in the tourist destination of Belgrade.

H2a. 

Awareness of consequences has a positive effect on personal norms among tourists in the tourist destination of Almaty.

H2b. 

Awareness of consequences has a positive effect on personal norms among tourists in the tourist destination of Belgrade.

H3a. 

Ascription of responsibility has a positive effect on behavioural intentions among tourists in the tourist destination of Almaty.

H3b. 

Ascription of responsibility has a positive effect on behavioural intentions among tourists in the tourist destination of Belgrade.

H4a. 

Personal norms have a positive effect on behavioural intentions among tourists in the tourist destination of Almaty.

H4b. 

Personal norms have a positive effect on behavioural intentions among tourists in the tourist destination of Belgrade.

H5a. 

Behavioural intentions have a positive effect on sustainability outcomes among tourists in the tourist destination of Almaty.

H5b. 

Behavioural intentions have a positive effect on sustainability outcomes among tourists in the tourist destination of Belgrade.

H6a. 

Awareness of consequences (AC) has a positive effect on sustainability outcomes among tourists in the tourist destination of Almaty.

H6b. 

Awareness of consequences (AC) has a positive effect on sustainability outcomes among tourists in the tourist destination of Belgrade.

H7a. 

Ascription of responsibility (AR) has a positive effect on sustainability outcomes among tourists in the tourist destination of Almaty.

H7b. 

Ascription of responsibility (AR) has a positive effect on sustainability outcomes among tourists in the tourist destination of Belgrade.

According to the proposed hypotheses, the following research model is suggested (Figure 1):

3. Methodology

In this research, the Norm Activation Model (NAM), originally developed in previous research [83] and later expanded in fields such as environmental protection, tourism, and sustainable behaviour, was applied. The NAM assumes that individual behaviour is not conditioned solely by personal interests but also by internal norms, moral obligations, and awareness of consequences. In this study, the model was further extended with the construct of Sustainability Outcomes to establish the link between tourist security, digital technologies, and the long-term sustainability of destinations. The constructs employed in the research include Awareness of Consequences (AC), referring to awareness of the consequences of an unsafe environment and the lack of digital solutions; Ascription of Responsibility (AR), denoting the attribution of responsibility to tourists and institutions for respecting and implementing safety procedures; Personal Norms (PN), representing tourists’ personal norms and moral obligations related to behaviour that supports security and sustainability; Behavioral Intention (BI), referring to tourists’ intentions to choose, revisit, or recommend a destination that employs digital technologies to strengthen security and sustainability; and Sustainability Outcomes (SO), representing the effects arising from the integration of digital technologies and security systems into sustainable development, such as a stable inflow of tourists, strengthened trust within the local community, and a positive regional image. The measurement items were developed based on established NAM literature [75,76,80,81] and were adapted to the context of digital technologies in tourism security and sustainability. The AC items were reformulated to capture the consequences of unsafe use or absence of digital solutions, while the PN items focused on tourists’ moral obligation to adopt or respect digital systems. The SO construct was newly developed, drawing on literature on digital transformation and sustainability, to reflect long-term outcomes such as destination image, community trust, and economic stability. A pilot pre-test with a small group of respondents was conducted to ensure clarity and reliability, and a back-translation procedure was applied for the multilingual versions of the questionnaire.

For the empirical part of the study, a structured questionnaire was developed, with each construct operationalised through at least five items measured on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). The questionnaire included items examining tourists’ perceptions of the application of digital technologies in the field of security, their willingness to cooperate with institutions, personal norms related to safety and sustainability, behavioural intentions for the future, and expected long-term effects of digitalisation on destinations.

The research was conducted as a comparative case study in two urban destinations: Almaty in Kazakhstan and Belgrade in Serbia, during the period from January 2025 to July 2025. The selection of these destinations was based on their status as regional centres with increasing tourist flows, as well as on their different socio-economic and cultural contexts, which enable a deeper comparative analysis. The sample consisted of tourists who were staying in these cities at the time of the survey, and the sampling technique applied was convenience sampling. It was planned to collect between 200 and 250 valid questionnaires in each destination, ensuring sufficient statistical power for the application of multivariate analysis methods.

The data were collected by trained interviewers at airports, railway stations, tourist information centres, and hotels. In Kazakhstan, the questionnaire was available in Kazakh, Russian, and English, while in Serbia it was available in Serbian and English, in order to cover different tourist profiles. For data processing, a combination of statistical methods was employed, including descriptive statistics to describe the sample and basic variables, partial least squares structural equation modelling (PLS-SEM) to test the relationships between constructs and verify hypotheses, as well as Multi-Group Analysis (MGA). This combination made it possible to analyse the results both at the descriptive level and within complex causal relationships, thus contributing to the reliability and validity of the research. The factors were adapted from the Norm Activation Model [75,76,83,84], with the addition of the construct of Sustainability Outcomes based on sustainable development goals and contemporary literature. The only conditions for the anonymous participation of respondents were that they were of legal age and that they were tourists in Almaty (KZ) or Belgrade (RS), meaning individuals without permanent residence in these cities.

4. Results

A total of 435 respondents participated in the study (

Table 1. Demographic Profile of Respondents.

		Almaty (KZ)		Belgrade (RS)		Total
		N	%	N	%	N	%
Gender	Male	132	63.8	89	39.0	221	50.8
	Female	75	36.2	139	61.0	214	49.2
Age	18–24	36	17.4	37	16.2	73	16.8
	25–34	65	31.4	72	31.6	137	31.5
	35–44	40	19.3	45	19.7	85	19.5
	45–54	32	15.5	38	16.7	70	16.1
	55+	34	16.4	36	15.8	70	16.1
Education	High school	61	29.5	82	36.0	143	32.9
	College or University Degree	88	42.5	113	49.6	201	46.2
	Master or PhD	58	28.0	33	14.5	91	20.9

Source: Author’s research.

), with 207 from Almaty (KZ) and 228 from Belgrade (RS). In terms of gender, the sample structure in Almaty consisted predominantly of men (63.8%) compared to women (36.2%), while in Belgrade the situation was the opposite—women constituted the majority (61.0%), and men accounted for 39.0%. At the level of the overall sample, the gender distribution was almost balanced (men 50.8%, women 49.2%).

Regarding age distribution, the largest group in both cities belonged to the 25–34 age category (Almaty: 31.4%; Belgrade: 31.6%), representing nearly one-third of the sample (31.5%). They were followed by the groups aged 35–44 (19.5%) and 45–54 (16.1%), while the younger respondents aged 18–24 (16.8%) and the older group aged 55 and above (16.1%) were represented in nearly equal proportions. These data indicate that the sample was relatively balanced across age categories, with a dominance of younger adults.

As for the level of education, the majority of respondents had completed university studies (Almaty: 42.5%; Belgrade: 49.6%; overall: 46.2%). Secondary education was reported by 32.9% of participants, while the smallest group consisted of those with completed master’s or doctoral studies (20.9%). This demonstrates that the sample was predominantly highly educated, which is consistent with the profile of tourists who more frequently use digital technologies in their travel experience.

Table 2. Descriptive Statistics for Items of Constructs.

Factor	Item	Almaty (KZ)		Belgrade (RS)
		M	SD	M	SD
AC	ac1: If a destination does not apply digital technologies (smart surveillance, applications), tourists may have negative experiences.	3.73	1.356	3.55	1.503
	ac2: The lack of modern security systems can undermine the image of a destination.	3.60	1.444	3.39	1.571
	ac3: Failures in the application of digital technologies lead to economic losses in tourism.	3.84	1.308	3.70	1.369
	ac4: A low level of digital security reduces the likelihood that tourists will recommend the destination.	3.68	1.412	4.09	1.492
	ac5: The tourism industry suffers in the long term if the implementation of digital security solutions is neglected.	4.16	1.174	4.20	1.226
AR	ar1: I feel personally responsible for using digital applications and complying with safety protocols during travel.	4.22	1.188	4.05	1.264
	ar2: I believe it is my duty to cooperate with institutions and use digital systems for safety.	3.98	1.117	4.22	1.089
	ar3: If I do not use digital technologies that enhance security (e.g., mobile applications), I put both myself and other tourists at risk.	3.22	1.171	4.10	1.151
	ar4: Tourists should support institutions in maintaining security through the use of available digital tools.	3.10	1.188	3.84	1.144
	ar5: My behaviour regarding the application of digital technologies can influence the level of security in a destination.	4.12	1.181	3.95	1.325
PN	pn1: I feel a moral obligation to support destinations that implement digital security systems.	3.85	1.123	4.11	1.255
	pn2: Ignoring digital security measures would be wrong on my part.	3.64	1.407	4.19	1.166
	pn3: I always strive to use and comply with digital tools and procedures during travel.	3.78	1.325	3.85	1.293
	pn4: My conscience tells me that I should respect institutions that enhance security through the application of digital technologies.	3.90	1.238	3.44	1.209
	pn5: Supporting digital security solutions is my personal responsibility as a tourist.	3.44	1.279	4.05	1.243
BI	bi1: I would deliberately choose a destination that uses digital technologies to ensure security.	3.82	1.381	3.25	1.373
	bi2: I am willing to revisit a destination where I felt safe thanks to digital systems.	3.79	1.273	4.21	1.055
	bi3: I would recommend to friends a destination that uses modern digital tools for security and sustainability.	3.87	1.208	3.58	1.066
	bi4: My priority is to use airports and infrastructure where effective digital control measures are in place.	3.90	1.224	3.12	1.102
	bi5: The level of digital security strongly influences my decision when choosing a destination.	3.87	1.338	3.45	1.276
SO	so1: Destinations that implement digital security systems have a stable inflow of tourists.	3.74	1.199	3.26	1.451
	so2: Investments in digital technologies support the local economy in the long term.	3.95	1.225	3.15	1.333
	so3: The sense of security provided by digital systems strengthens trust between tourists and the local community.	3.89	1.298	3.95	1.356
	so4: The application of digital security reduces prejudice and enhances the positive image of the region.	4.05	1.199	3.23	1.325
	so5: Advanced digital systems demonstrate the institutional maturity of a destination.	3.90	1.288	3.73	1.425
	so6: Destinations that guarantee digital security have greater prospects for sustainable tourism.	3.79	1.183	3.11	1.125
	so7: The perception of digital security encourages long-term tourist loyalty.	3.88	1.943	3.97	1.345

Source: Author’s research.

presents the detailed results of descriptive statistics for all items of the research instrument for the samples in Almaty (KZ) and Belgrade (RS). Within the factor Awareness of Consequences (AC), the mean values in both cities ranged between 3.39 and 4.20. Respondents in Almaty expressed the highest level of agreement with the statement that the tourism industry suffers in the long term if the implementation of digital security solutions is neglected (M = 4.16), while in Belgrade the same statement received the strongest support (M = 4.20). The lowest mean values were recorded for item AC2 in both cities (M = 3.60 in Almaty; M = 3.39 in Belgrade), suggesting that respondents were somewhat more moderate in their views that the lack of modern security systems directly undermines the image of a destination.

For the factor Ascription of Responsibility (AR), Almaty respondents demonstrated strong agreement with personal responsibility in using digital applications and complying with safety protocols (ar1, M = 4.22), while in Belgrade the highest support was recorded for the duty to cooperate with institutions through the use of digital systems (ar2, M = 4.22). Interestingly, the values for ar3 and ar4 in Almaty were notably lower (M = 3.22; M = 3.10) compared to Belgrade (M = 4.10; M = 3.84), indicating that tourists in Serbia are more inclined to accept collective responsibility in the application of digital safety tools.

Regarding Personal Norms (PN), in Belgrade the strongest agreement was related to the moral condemnation of ignoring digital safety measures (pn2, M = 4.19), whereas in Almaty the highest value was attributed to the sense of moral obligation to support destinations applying digital systems (pn1, M = 3.85). The lowest mean value in Almaty was recorded for pn5 (M = 3.44), while in Belgrade the same indicator received significantly higher ratings (M = 4.05), reflecting a different degree of internalisation of personal responsibility between the two contexts.

For the factor Behavioral Intention (BI), mean values in Almaty were relatively consistent (3.79–3.90), whereas in Belgrade stronger variations appeared: the highest agreement referred to the willingness to revisit a destination with digital systems (bi2, M = 4.21), while the lowest value was given to the prioritisation of airports and infrastructure with digital control measures (bi4, M = 3.12). This indicates that tourists in Serbia place greater emphasis on personal experiences of security than on infrastructural aspects.

Within the factor Sustainability Outcomes (SO), respondents in Almaty showed the highest agreement with the statement that the implementation of digital security strengthens the positive image of the region (so4, M = 4.05), while in Belgrade the highest rating was recorded for the statement that digital security encourages long-term tourist loyalty (so7, M = 3.97). The lowest values in both cities referred to the economic aspect (so2, M = 3.95 in Almaty; M = 3.15 in Belgrade), which may indicate lower awareness among respondents of the long-term financial effects of digital investments in tourism.

Table 3. Reliability and Validity Statistics for Constructs.

Factors	Abbreviated	Scale Statistics	Almaty (KZ)	Belgrade (RS)
Awareness of Consequences	AC	Cronbach’s α	0.910	0.876
		CR	0.90	0.88
		AVE	0.65	0.68
		M	3.80	3.79
		SD	1.150	1.162
Ascription of Responsibility	AR	Cronbach’s α	0.922	0.794
		CR	0.92	0.85
		AVE	0.70	0.68
		M	3.73	4.03
		SD	1.165	1.171
Personal Norms	PN	Cronbach’s α	0.844	0.749
		CR	0.95	0.86
		AVE	0.88	0.74
		M	3.72	3.93
		SD	1.102	1.225
Behavioral Intention	BI	Cronbach’s α	0.812	0.906
		CR	0.88	0.92
		AVE	0.60	0.64
		M	3.85	3.52
		SD	1.195	1.215
Sustainability Outcomes	SO	Cronbach’s α	0.877	0.845
		CR	0.91	0.87
		AVE	0.63	0.69
		M	3.89	3.49
		SD	1.125	1.164
CR—Composite Reliability; AVE—Average Variance Extracted; M—Mean; SD—Standard Deviation

Source: Author’s research.

presents the results of descriptive statistics, as well as the indicators of reliability and convergent validity for the five constructs used in the research through factor analysis Awareness of Consequences (AC), Ascription of Responsibility (AR), Personal Norms (PN), Behavioral Intention (BI), and Sustainability Outcomes (SO)—separately for the samples in Almaty (KZ) and Belgrade (RS). The values of Cronbach’s α for all constructs exceeded the recommended threshold of 0.70, indicating satisfactory internal consistency. The highest reliability was recorded for the construct AR in Almaty (α = 0.922), while the lowest value of α = 0.749 was observed for PN in Belgrade, though it still remained within acceptable limits. In addition, the Composite Reliability (CR) values ranged between 0.85 and 0.95, confirming high construct stability. All constructs also demonstrated satisfactory values of Average Variance Extracted (AVE), which exceeded the 0.50 threshold (ranging from 0.60 to 0.88), thereby confirming convergent validity.

Regarding the mean values (M), they ranged between 3.49 and 4.03, indicating a moderately high level of agreement among respondents with statements related to the application of digital technologies in the context of security and sustainability of tourist destinations. In Almaty, the construct SO recorded the highest score (M = 3.89), while in Belgrade the highest value was observed for AR (M = 4.03). The lowest mean values were recorded for BI in Belgrade (M = 3.52) and PN in Almaty (M = 3.72). The standard deviations (SD) ranged between 1.10 and 1.22, suggesting relatively consistent responses with moderate variability. These results indicate that respondents in both cities recognised the importance of digital technologies for destination security and sustainability, with practical aspects (SO) being more emphasised in Almaty, while a stronger sense of personal responsibility (AR) was highlighted in Belgrade.

Table 4. Composite Reliability (CR) and Average Variance Extracted (AVE) for Constructs.

Construct	CR		AVE
	Almaty	Belgrade	Almaty	Belgrade
Awareness of Consequences (AC)	0.93	0.93	0.727	0.731
Ascription of Responsibility (AR)	0.96	0.96	0.831	0.833
Personal Norms (PN)	0.96	0.96	0.841	0.840
Behavioral Intention (BI)	0.90	0.89	0.651	0.620
Sustainability Outcomes (SO)	0.92	0.92	0.622	0.619

Source: Author’s research.

presents the indicators of reliability and convergent validity for the five constructs in the two samples through SEM analysis. The CR values were exceptionally high across all constructs (0.89–0.96), indicating stable internal consistency of the measurement scales. Particularly noteworthy are AR and PN (both = 0.96 in both cities), suggesting that the indicators uniformly and reliably reflect the latent construct. Although CR values around 0.96 may raise concerns regarding possible redundancy of some items, within the context of this study such values are acceptable given the clear theoretical coherence and the high loading levels of the indicators. The AVE values for all constructs exceeded the reference threshold of 0.50, thereby confirming convergent validity. The highest convergent validity was recorded for PN (AVE = 0.841–0.840), followed by AR (0.831–0.833) and AC (0.727–0.731), indicating that these constructs explain a substantial proportion of the variance in their indicators. BI and SO demonstrated moderate but clearly acceptable AVE values (BI: 0.651 in Almaty and 0.620 in Belgrade; SO: 0.622 and 0.619), which is common for constructs capturing behavioural intentions and broader perceptions of outcomes. Such values indicate that any slightly weaker individual items were successfully absorbed at the factor level without compromising validity.

The comparison between the two cities demonstrates strong stability of the measurement model: differences in CR and AVE were minimal (most often ≤0.03), allowing the conclusion that the psychometric properties of the scales were practically identical in both contexts. This is significant as it supports the comparability of results (cross-cultural/cross-contextual consistency) and suggests that observed differences in structural relationships (e.g., the effects of AC/AR on BI and SO) do not stem from measurement artefacts but instead reflect genuine variations in respondents’ behaviours and attitudes.

The measurement model meets the key criteria: high reliability (CR ≥ 0.89) and valid convergence (AVE ≥ 0.619) across all constructs and in both samples. These findings justify moving to the evaluation of the structural model (path coefficients, R², f², Q², and bootstrapping) and the testing of the proposed hypotheses. If further evidence of validity is required, it is advisable to also present discriminant validity (Fornell–Larcker and HTMT). However, based on the reported indicators, the measurement model can be considered statistically and theoretically adequate.

Table 5. Fornell–Larcker Criterion for Discriminant Validity.

Construct	Almaty (KZ)					Belgrade (RS)
	AC	AR	PN	BI	SO	AC	AR	PN	BI	SO
AC	0.85					0.84
AR	0.65	0.91				0.64	0.90
PN	0.58	0.62	0.92			0.57	0.61	0.91
BI	0.54	0.60	0.63	0.81		0.53	0.59	0.62	0.78
SO	0.67	0.64	0.61	0.66	0.79	0.66	0.63	0.60	0.65	0.78

Source: Author’s research.

presents the assessment of discriminant validity using the Fornell–Larcker criterion for both samples. The values on the diagonal represent the square root of AVE for each construct, while the off-diagonal values represent the correlations between constructs. The basic requirement of discriminant validity is that the square root of AVE for each construct must be greater than any of its correlations with other constructs. The results show that all diagonal values are greater than the corresponding inter-factor correlations, both in the Almaty sample and in the Belgrade sample.

For example, in Almaty the square root of AVE for the construct AC was 0.85, which exceeded its correlations with AR (0.65), PN (0.58), BI (0.54), and SO (0.67). A similar pattern was observed in Belgrade, where the square root of AVE for AC was 0.84, also higher than all its correlations with other constructs. All other constructs, including AR, PN, BI, and SO, demonstrated the same consistency—the diagonal values (0.90–0.92 in Almaty; 0.78–0.91 in Belgrade) exceeded all off-diagonal values. This confirms that each construct measures a unique phenomenon and that there is no overlap between different constructs.

The comparison of the two samples reveals a high degree of similarity in the correlation structure. In both contexts, the highest correlations were observed between AC and SO (0.67 in Almaty; 0.66 in Belgrade), indicating a close relationship between awareness of consequences and the perception of sustainability outcomes. On the other hand, the lowest correlations were recorded between AC and BI (0.54 in Almaty; 0.53 in Belgrade), suggesting that awareness of consequences has a relatively limited direct connection with behavioural intention, but that this relationship may be mediated by other factors.

Table 6. Heterotrait–Monotrait Ratio (HTMT) for Discriminant Validity.

Construct	Almaty (KZ)					Construct	Belgrade (RS)
	AC	AR	PN	BI	SO		AC	AR	PN	BI	SO
AC	–					AC	–
AR	0.71	–				AR	0.70	–
PN	0.65	0.69	–			PN	0.64	0.67	–
BI	0.61	0.67	0.70	–		BI	0.60	0.68	0.68	–
SO	0.72	0.69	0.66	0.71	–	SO	0.70	0.65	0.64	0.69	–

Source: Author’s research.

presents the HTMT values as an additional test of discriminant validity for both samples. In both cases, all values ranged from 0.60 to 0.72. These results are well below the reference thresholds of 0.90 (the stricter criterion) or 0.85 (the conservative criterion), indicating that discriminant validity is not compromised and that each construct measures a distinct phenomenon. In the Almaty sample, the highest HTMT value was observed between AC and SO (0.72), confirming the close association between awareness of consequences and the perception of sustainability outcomes. The lowest values were recorded for the relationships AC–BI (0.61) and PN–SO (0.66), suggesting relatively moderate connections between these constructs. In Belgrade, the pattern was similar but with slightly lower values. The strongest associations were again between AC and SO (0.70) and BI and SO (0.69), indicating that respondents in Serbia also recognised the importance of digital security for long-term sustainability and for their own behaviour in tourism. The weakest relationship was recorded between AC and BI (0.60), confirming that awareness of consequences has a more limited direct effect on behavioural intentions compared to indirect factors such as personal norms and ascription of responsibility.

Figure 2 illustrates the structural model obtained through PLS-SEM analysis for the Almaty sample. The model presents the relationships among the five latent constructs. The values next to the indicators (ac1–ac5, ar1–ar5, pn1–pn5, bi1–bi5, so1–so7) represent the outer loadings, which indicate how well individual items reflect their respective constructs. All values exceeded the recommended threshold of 0.70, demonstrating high convergent validity and measurement reliability.

The arrows between latent variables display the path coefficients (β), which reflect the strength and direction of the effects between constructs. The strongest effects were observed for the relationships AC → PN (β = 0.837) and AR → SO (β = 0.736), showing that awareness of consequences strongly influences the formation of personal norms, while ascription of responsibility exerts a direct and substantial effect on sustainability outcomes. In addition, significant relationships were also found for AR → BI (β = 0.851) and SO → BI (β = 0.808), suggesting that both tourists’ sense of responsibility and their perception of sustainability strongly encourage their behavioural intentions.

Figure 3 presents the results of the structural model obtained through PLS-SEM analysis for the Belgrade sample. The outer loadings of the indicators (ac1–ac5, ar1–ar5, pn1–pn5, bi1–bi5, so1–so7) were generally above 0.70, confirming good convergent validity and instrument reliability. Although pn1 and pn2 showed slightly lower values (~0.64), they were still acceptable, as they did not compromise the overall reliability of the construct. The key path coefficients (β) indicate strong and statistically significant relationships between the constructs. The strongest effects were observed for AR → SO (β = 0.727) and AR → BI (β = 0.717), demonstrating that ascription of responsibility is a crucial factor in shaping both sustainability outcomes and tourists’ behavioural intentions. The relationships AC → AR (β = 0.739) and AC → PN (β = 0.660) were also significant, suggesting that awareness of consequences directly strengthens both responsibility and personal norms. The relationship PN → BI (β = 0.606) shows that tourists’ moral norms significantly influence their behavioural intentions, although with somewhat lower strength compared to the direct effects of AR and SO.

The results of the bootstrapping analysis (

Table 7. Hypotheses testing results.

Hypothesis	Path	Almaty (KZ)
		β	t-Value	p-Value	Result
H1a	AC → AR	0.739	12.35	0.001	Supported
H2a	AC → PN	0.660	10.28	0.002	Supported
H3a	AR → PN	0.733	11.12	0.007	Supported
H4a	AR → BI	0.717	13.47	0.011	Supported
H5a	SO → BI	0.632	9.86	0.006	Supported
H6a	PN → BI	0.606	8.55	0.001	Supported
H7a	AR → SO	0.727	14.21	0.012	Supported
H8a	AC → SO	0.133	1.22	0.022	Supported
Hypothesis	Path	Belgrade (RS)
		β	t-Value	p-Value	Result
H1b	AC → AR	0.722	13.45	0.000	Supported
H2b	AC → PN	0.713	11.12	0.001	Supported
H3b	AR → PN	0.781	13.55	0.001	Supported
H4b	AR → BI	0.766	13.22	0.016	Supported
H5b	SO → BI	0.695	10.86	0.002	Supported
H6b	PN → BI	0.716	9.43	0.003	Supported
H7b	AR → SO	0.827	12.29	0.011	Supported
H8b	AC → SO	0.233	5.32	0.005	Supported

Source: Author’s research.

) confirmed that all proposed hypotheses were statistically supported (p < 0.05) in both samples, with positive and generally medium to high path coefficients. In both contexts, awareness of consequences (AC) had a significant effect on ascription of responsibility (AR) and personal norms (PN), thereby confirming the theoretical foundations of the NAM greater perception of potential risks and negative outcomes directly strengthens tourists’ sense of duty and internalised moral obligations. Ascription of responsibility then strongly influenced personal norms, reinforcing the normative channel of influence within the model. With regard to behavioural intentions (BI), in both cities the strongest effect originated from ascription of responsibility (Almaty β = 0.717; Belgrade β = 0.766), indicating that tourists who feel personally responsible show a greater willingness to behave in line with safety and sustainability guidelines. In addition, personal norms (Almaty β = 0.606; Belgrade β = 0.716) and perceptions of sustainability outcomes (SO) (Almaty β = 0.632; Belgrade β = 0.695) had significant effects on BI. This means that tourists’ moral obligations and the perceived benefits of digital security for the destination (economic stability, community trust, positive image) strongly encourage their intentions to choose and recommend the destination.

The relationship between AR and SO (Almaty β = 0.727; Belgrade β = 0.827) was particularly significant, showing that a sense of personal responsibility directly fosters positive and sustainable outcomes, thereby strengthening the economic, social, and image dimensions of the tourism offer. In Almaty, the relationship between AC and SO (β = 0.133) was very weak and likely mediated through AR and PN, whereas in Belgrade this relationship was considerably stronger and statistically significant (β = 0.233), which may indicate a greater sensitivity among tourists in Serbia to information about the consequences of insecurity. The results for both samples confirm the strong applicability of the NAM in the context of digital technologies in tourism. Awareness of consequences activates responsibility and personal norms, which, together with perceptions of sustainability outcomes, shape tourists’ intentions. In Almaty, sustainability outcomes had the most pronounced effect on intentions, while in Belgrade the dominant effects came from personal responsibility, reflecting cultural and contextual differences in the acceptance of digital security as a factor of sustainable destination development. From a practical perspective, interventions that highlight the consequences of risks, strengthen tourists’ sense of personal responsibility, and make the positive effects of digital safety measures visible are most likely to lead to greater loyalty, destination choice, and recommendation.

The results of the Multi-Group Analysis (MGA) (

Table 8. Results of Multi-Group Analysis (MGA) between Almaty and Belgrade.

Hypothesis	Path	β (Almaty)	β (Belgrade)	p-Value (MGA)
H1a + H1b	AC → AR	0.739	0.722	0.003
H2a + H2b	AC → PN	0.660	0.713	0.001
H3a + H3b	AR → PN	0.733	0.781	0.011
H4a + H4b	AR → BI	0.717	0.766	0.110
H5a + H5b	SO → BI	0.632	0.695	0.055
H6a + H6b	PN → BI	0.606	0.716	0.004
H7a + H7b	AR → SO	0.727	0.827	0.019
H8a + H8b	AC → SO	0.133	0.233	0.002

Source: Author’s research.

) enabled a direct comparison of path coefficients between the samples from Almaty (KZ) and Belgrade (RS), thereby providing insight into whether there are statistically significant differences in the strength and direction of relationships within the proposed model. The analysis shows that although the model as a whole functions in both contexts, there are clear differences reflecting specific cultural and institutional characteristics. Significant differences were identified in the relationships AC → AR (p = 0.003), AC → PN (p = 0.001), and AR → PN (p = 0.011). These findings indicate that awareness of consequences exerts a stronger influence on ascription of responsibility and the formation of personal norms among respondents in Belgrade than in Almaty. Practically, tourists in Serbia more strongly internalise the consequences of insecurity and transform them into a sense of personal responsibility and moral obligation. In Almaty, this effect is also present but with slightly lower intensity, which may be the result of different social and institutional conditions or varying levels of development in digital security systems.

Significant differences were also observed in the paths PN → BI (p = 0.004), AR → SO (p = 0.019), and AC → SO (p = 0.002). Among respondents in Belgrade, personal norms had a stronger impact on behavioural intentions, suggesting that in the Serbian context internal moral mechanisms play a decisive role in shaping tourists’ willingness to revisit or recommend a destination. At the same time, ascription of responsibility had a stronger direct effect on sustainability outcomes in Belgrade, indicating that responsible tourist behaviour contributes to greater community trust, a stronger image, and more stable tourism development. Particularly noteworthy is the finding regarding AC → SO, where the effect in Almaty was weak (β = 0.133), while in Belgrade it was stronger and statistically significant (β = 0.233). This suggests that tourists in Serbia directly associate awareness of consequences with the perception of sustainability, whereas in Kazakhstan this effect functions mainly indirectly through other constructs.

The relationships AR → BI (p = 0.110) and SO → BI (p = 0.055) did not show statistically significant differences between Almaty and Belgrade. This means that in both contexts the pattern of influence of ascription of responsibility and sustainability outcomes on behavioural intentions is consistent and stable, indicating the universality of these relationships regardless of cultural specificities. In other words, tourists in both cities, when recognising their responsibility or perceiving positive sustainability outcomes, consistently show a greater willingness to revisit or recommend the destination.

The results of the MGA show that the NAM performs well in both samples, but with certain differences in the intensity of specific paths. In Belgrade, personal responsibility and moral norms emerged as stronger mechanisms influencing behavioural intentions and sustainability outcomes, while in Almaty these relationships were present but with lower values. Furthermore, in the Serbian sample, awareness of consequences was more directly linked to the perception of sustainability, whereas in the Kazakhstani context this relationship remained weak and mediated. These findings highlight cultural and institutional differences in the way tourists perceive and adopt digital security technologies as factors of sustainable destination development.

5. Discussion

This research aimed to examine the role of digital technologies in strengthening the security and sustainability of tourist destinations through the application of the Norm Activation Model (NAM). The results show that the NAM was fully supported in both samples, confirming its strength and applicability in the context of contemporary tourism. All constructs awareness of consequences (AC), ascription of responsibility (AR), personal norms (PN), behavioural intentions (BI), and perceptions of sustainability outcomes (SO) demonstrated significant and positive interrelationships, thereby confirming the initial assumption that digital technologies play a crucial role in creating a safe and sustainable tourism environment.

The results of the Multi-Group Analysis (MGA) provide important insights into the differences between Almaty (KZ) and Belgrade (RS). Although all hypotheses were confirmed in both samples, the analysis revealed variations in the strength of specific relationships. In Belgrade, the effects AC → AR and AC → PN were stronger, indicating that tourists in Belgrade more strongly translate risk awareness into a sense of responsibility and moral obligation. The stronger effects of AC → AR and AC → PN observed in Belgrade can be explained by the broader socio-cultural and institutional context of Serbia. As a post-socialist society with strong traditions of collectivism and solidarity, Serbia places strong emphasis on shared responsibility and moral obligations, further reinforced by public debates on sustainability and transparency driven by the country’s European integration. By contrast, in Kazakhstan, where Almaty has undergone rapid modernisation and digitalisation, responsibility is more often associated with institutional authority rather than internalised moral norms. Public awareness of digital security and sustainability is still developing, and tourists may rely more on top-down institutional assurances than on individual responsibility. These contextual differences illustrate that the explanatory power of NAM is moderated by historical, cultural, and institutional factors that shape the way individuals interpret and act upon digital security and sustainability challenges. These findings are consistent with the classical works of Stern (2000) and Han (2014) [75,84], which showed that the NAM consistently explains environmental and responsible behaviour. However, in this study the effect was analysed through the lens of digital technologies, representing an important extension of the existing theoretical framework.

Differences were particularly evident in the relationships AR → SO and AC → SO. In Belgrade these paths were stronger, indicating that tourists more closely link personal responsibility and awareness of consequences with broader sustainability outcomes such as a positive destination image, a stable local economy, and community trust. In Almaty these effects were present but weaker. Similar findings were reported by a previous study [77] on tourists’ ecological behaviour in China, where cultural context strongly shaped the influence of NAM constructs. Our results contribute to this line of research by showing that in the domain of digital technologies, cultural and institutional factors also shape the strength of relationships.

These findings demonstrate that the contribution of this study extends beyond showing that differences exist between the two destinations. By integrating Sustainability Outcomes into the NAM and applying it in a comparative setting, the research reveals how cultural and institutional contexts condition the strength of key relationships between awareness, responsibility, norms, and sustainability. This advances theoretical understanding by showing that NAM is not only a universal explanatory model but one whose mechanisms vary in intensity depending on socio-cultural realities. At the same time, the results provide practical guidance for destination managers and policymakers: in contexts such as Belgrade, strategies should focus on engaging tourists through personal responsibility and co-creation of sustainable practices, while in contexts such as Almaty, stronger institutional communication and policy support are needed to raise awareness of the consequences of insecurity and promote the adoption of digital technologies.

Certain relationships did not show significant differences between Almaty and Belgrade. The links AR → BI and SO → BI were stable in both samples, pointing to the universality of these constructs. These results confirm the findings of Onwezen et al. (2013) [76] who emphasised that personal responsibility and positive outcomes are consistent determinants of behaviour across cultural settings. In this research it was shown that this universality also applies in the field of digital tourism, which had not been empirically tested before. Comparisons with recent works on smart destinations and tourism digitalisation further reinforce these findings. Buhalis & Amaranggana (2015) [78] emphasised that digital technologies form the foundation of the smart destination concept, as they integrate infrastructure, security, and sustainability. Xiang et al. (2017) [79] highlighted the importance of big data analytics and mobile applications in creating safer tourism environments. Our research confirms these findings but also extends them by showing that the effects of digital technologies are not uniform across socio-cultural contexts. In Belgrade, for example, tourists more strongly associate digital security with sustainability, whereas in Almaty this link is weaker and mediated by other factors.

Moreover, the results are in line with studies from other parts of the world that analysed the NAM in tourism. Han (2014) [75] demonstrated that NAM successfully explains tourists’ intentions to support environmental policies in the hotel industry. Zhu et al. (2022) [80] applied NAM in the context of cultural tourism in South Korea and reached similar conclusions personal norms and responsibility remain key predictors of behaviour. Our study, however, adds a new dimension by showing that digital security and sustainability represent a domain in which NAM retains equally strong explanatory power. The findings of this research are aligned with the rich body of literature confirming NAM as a suitable model for studying environmental, socially responsible, and tourism behaviour, but they extend it by showing that the same mechanisms also operate in the domain of digital technologies. At the same time, the MGA reveals that cultural and institutional differences influence the strength of specific relationships, making this study the first attempt to test NAM in a comparative context of the digital transformation of tourism.

5.1. Limitations of the Research

Although this research has provided important insights into the role of digital technologies in strengthening the security and sustainability of tourist destinations, several limitations should be noted. First, the study was conducted on two urban samples, Almaty (KZ) and Belgrade (RS). While this offers a valuable comparative framework, it at the same time limits the generalisability of the findings to other contexts, particularly rural destinations or those at different stages of digital transformation. The cultural, institutional, and economic specificities of these two cities may significantly influence tourists’ perceptions, and the results cannot be automatically extended to all tourism environments.

The study relied on self-reported data collected through a structured questionnaire, which carries the risk of subjectivity, socially desirable responses, and potential discrepancies between declared intentions and actual behaviour. In addition, the research applied a cross-sectional methodology, meaning that the results reflect a single moment in time and do not allow for tracking dynamic changes in tourists’ attitudes and behaviours. Moreover, since the data were collected using a single instrument, the possibility of common method bias cannot be excluded, even though statistical checks indicated that collinearity remained within acceptable thresholds.

A further limitation concerns the choice of model. Although the Norm Activation Model (NAM) proved highly applicable in this context, other relevant factors such as perceived usefulness of technology, the level of digital literacy, or cultural values were not included, even though they could further explain differences in tourist behaviour. In addition, although a multi-group analysis was performed, future research should also systematically test measurement invariance (e.g., MICOM procedure) when conducting cross-cultural comparisons. Integrating additional theoretical frameworks such as the Theory of Planned Behaviour (TPB) or the Technology Acceptance Model (TAM) could also provide a more comprehensive picture. While this study covered two distinct geographical and cultural contexts, future research should include a larger number of destinations and adopt longitudinal as well as mixed-methods designs. Combining quantitative surveys with qualitative approaches, such as interviews or case studies, would provide deeper insights into tourists’ attitudes and experiences regarding the use of digital technologies in tourism.

Limitation relates to the demographic structure of the sample. The gender distribution varied considerably between the two destinations, with a predominance of male respondents in Almaty and female respondents in Belgrade. Moreover, the overall educational level of the participants was relatively high, with more than two-thirds holding a university degree or postgraduate qualification. These characteristics indicate that the samples may not be fully representative of the broader tourist populations in either city. Given that convenience sampling was applied at key transport hubs and tourist locations, caution is required when generalising the findings beyond the studied contexts.

5.2. Theoretical Implications

This research has important theoretical implications as it extends the application of the Norm Activation Model (NAM) to the domain of digital technologies in tourism. While NAM was originally developed to explain environmental and prosocial behaviour [83,84] the findings of this study demonstrate that the same mechanisms operate in the context of digital security and the sustainability of tourist destinations. By confirming all proposed hypotheses in two different cultural and institutional settings, the study expands the existing body of knowledge and points to the universality of the NAM in contemporary tourism. One of the key implications is the confirmation that the constructs awareness of consequences (AC), ascription of responsibility (AR), and personal norms (PN) remain central mechanisms in shaping behavioural intentions (BI) as well as in forming perceptions of sustainability outcomes (SO). In this way, the study contributes to the literature by extending NAM from its traditional focus on environmental and socially responsible behaviour to the field of digital technologies, showing that the digital transformation of tourism can be explained by the same psychological mechanisms.

The results of the Multi-Group Analysis (MGA) reveal that although NAM is universal, its intensity varies across cultural contexts. In Belgrade, the paths AC → AR, AC → PN, and PN → BI were stronger, whereas in Almaty these relationships were more moderate. This confirms previous findings [75,76,83,84] that cultural and institutional contexts can shape the strength of relationships in NAM, but for the first time demonstrates that such variations also occur in the context of digital security technologies. The theoretical implications also include the extension of the NAM through the incorporation of the construct of sustainability outcomes (SO). While NAM in its original form was primarily focused on moral norms and behaviour, this study shows that digital technologies as a factor of security have a direct impact on the perception of sustainability. In this way, the model has been advanced with an additional dimension that links individual behaviour to broader social and economic outcomes.

5.3. Practical Implications

This research carries important practical implications for destination management and the development of digital transformation strategies. The confirmed relationships within the NAM indicate that digital technologies are not merely tools for service improvement but essential mechanisms for building security and long-term sustainability. First, the results show that awareness of consequences and personal responsibility play a central role in shaping tourist behaviour. This means that destination managers and tourism institutions must more actively inform tourists about the importance of digital security solutions. Clear communication on how smart surveillance systems, mobile applications, and IoT devices enhance safety can encourage tourists to use them actively and foster greater trust in the destination.

The comparative analysis between Almaty and Belgrade revealed that tourists in Serbia are more sensitive to moral norms and responsibility, while in Kazakhstan these links are somewhat weaker. This suggests that, in the Serbian context, it is necessary to emphasise tourists’ personal contribution to maintaining safety and sustainability, whereas in Kazakhstan institutions should invest more in raising awareness of the consequences of insecurity and more actively demonstrate the benefits of digital systems. Tailoring strategies to the specific cultural context thus proves essential for the effective implementation of digital technologies.

The results further demonstrated that tourists directly associate digital security with perceptions of sustainability. This implies that digital technologies should not be regarded merely as costs but as investments that build long-term loyalty, positive image, and stable economic development. Destinations that integrate digital tools into their strategies—from mobile applications for communication with tourists to artificial intelligence systems for real-time data analysis—are more likely to establish a competitive advantage.

For destination managers, these findings highlight the importance of designing training programmes for staff, introducing participatory applications that allow tourists to co-create safe and sustainable experiences, and strengthening transparent communication about digital safety measures. For public policymakers, the study emphasises the need to develop supportive regulatory frameworks, offer incentives for investments in digital infrastructure, and promote digital literacy through educational campaigns. By coordinating institutional support with managerial strategies, destinations can ensure that the adoption of digital technologies not only increases immediate safety but also contributes to the broader goals of sustainable tourism development.

5.4. Recommendations for Future Research

Although this research has provided valuable insights into the role of digital technologies in strengthening the security and sustainability of tourist destinations, several directions for future studies can be identified. It is recommended to extend the analysis to a larger number of destinations across different regions of the world in order to determine whether the results obtained can be generalised beyond Almaty and Belgrade. Including destinations with varying levels of digital transformation and institutional support would allow for broader comparative insights. Future research could also combine quantitative and qualitative methods. While this study was based on a survey questionnaire and statistical analysis, interviews with tourists, destination managers, and policymakers could provide deeper insights into perceptions of digital security and sustainability. Such a mixed-methods approach would allow for a better understanding of motivations, attitudes, and potential barriers in the adoption of digital solutions.

There is also room for expanding the theoretical framework. Although the Norm Activation Model (NAM) proved to be a strong explanatory tool, integration with other models such as the Theory of Planned Behaviour (TPB) or the Technology Acceptance Model (TAM) could offer a more comprehensive understanding of the processes behind the adoption of digital technologies in tourism. This would make it possible to explore additional factors such as digital literacy, perceived usefulness, or cultural values. Finally, future research could focus on longitudinal studies that track changes in tourists’ attitudes and behaviours over time. Such studies would allow researchers to observe how technological development, institutional measures, or global events (e.g., health crises or economic instabilities) influence perceptions of security and sustainability in tourism.

6. Conclusions

This study aimed to examine the role of digital technologies in enhancing the security and sustainability of tourist destinations through the application of the Norm Activation Model (NAM) in two different cultural and institutional contexts—Almaty (Kazakhstan) and Belgrade (Serbia). The results demonstrated that the NAM possesses strong explanatory power in the domain of digital tourism, confirming that the constructs of awareness of consequences, ascription of responsibility, and personal norms shape tourists’ behavioural intentions and influence their perceptions of sustainability outcomes. In this way, the study extends the existing body of knowledge and shows that digital technologies are not merely technological instruments but also psychological and social catalysts of responsible tourist behaviour. The comparative analysis between Almaty and Belgrade revealed significant differences in the intensity of specific r elationships within the NAM. In Belgrade, tourists were more inclined to transform awareness of consequences into personal and moral responsibility, and to more strongly connect digital security with broader sustainability outcomes. In Almaty, these relationships were weaker, suggesting the need for stronger institutional and educational mechanisms to enhance perceptions of the importance of digital security measures. These findings clearly indicate that, while the NAM is universal, its application and intensity depend on specific cultural and institutional contexts.

The research further showed that digital technologies such as mobile applications, smart surveillance systems, artificial intelligence, and IoT devices play a crucial role in creating safer and more sustainable tourism environments. What this study contributes as a novelty is the emphasis on cultural differences that shape the acceptance and interpretation of these technologies. The findings confirm the importance of digital technologies as a central factor in the transformation of tourism and demonstrate that their implementation directly affects not only the perception of safety but also the long-term sustainability of destinations. The comparison of Almaty and Belgrade provides valuable insights into how different socio-cultural contexts condition the adoption of digital solutions, thereby contributing to both theory and practice in the field of digital transformation in tourism.